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Stratified doubly robust estimators for the average causal effect
Author(s) -
Hattori Satoshi,
Henmi Masayuki
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12157
Subject(s) - propensity score matching , estimator , mathematics , statistics , weighting , econometrics , outcome (game theory) , causal inference , efficient estimator , minimum variance unbiased estimator , robust statistics , medicine , mathematical economics , radiology
Summary Suppose we are interested in estimating the average causal effect from an observational study. A doubly robust estimator, which is a hybrid of the outcome regression and propensity score weighting, is more robust than estimators obtained by either of them in the sense that, if at least one of the two models holds, the doubly robust estimator is consistent. However, a doubly robust estimator may still suffer from model misspecification since it is not consistent if neither of them is correctly specified. In this article, we propose an alternative estimator, called the stratified doubly robust estimator, by further combining propensity score stratification with outcome regression and propensity score weighting. This estimator allows two candidate models for the propensity score and is more robust than existing doubly robust estimators in the sense that it is consistent either if the outcome regression holds or if one of the two models for the propensity score holds. Asymptotic properties are examined and finite sample performance of the proposed estimator is investigated by simulation studies. Our proposed method is illustrated with the Tone study, which is a community survey conducted in Japan.